Triple

T15745886
Position Surface form Disambiguated ID Type / Status
Subject Rift Valley Province E381719 entity
Predicate containsCounty P5971 FINISHED
Object Narok County E318871 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Narok County | Statement: [Rift Valley Province, containsCounty, Narok County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Narok County
Context triple: [Rift Valley Province, containsCounty, Narok County]
  • A. Narok County chosen
    Narok County is a county in southwestern Kenya known for its vast savannah landscapes, rich Maasai culture, and world-famous wildlife tourism.
  • B. Nakuru County
    Nakuru County is a region in Kenya’s Rift Valley known for its lakes, wildlife, and agricultural activities.
  • C. Nyandarua County
    Nyandarua County is an administrative region in central Kenya known for its highland agriculture and proximity to the Aberdare Range.
  • D. Baringo County
    Baringo County is a largely rural county in Kenya’s Rift Valley region, known for its lakes, diverse ethnic communities, and semi-arid landscapes.
  • E. Marsabit County
    Marsabit County is a large, sparsely populated county in northern Kenya known for its arid landscapes, diverse ethnic communities, and proximity to major features like Lake Turkana and the Chalbi Desert.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0502d72008190b4d13a6b3a12e467 completed April 16, 2026, 2:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff28d38a4819085bf051b566c17e7 completed May 10, 2026, 2:50 a.m.
Created at: April 10, 2026, 4:46 a.m.